Comparison of Artificial Neural Network and Logistic Regression Models for Prediction of Psychological Symptom Six Months after Mild Traumatic Brain Injury

Shafiei, Elham; Fakharian, Esmaeil; Omidi, Abdollah; Akbari, Hossein; Delpisheh, Ali; Nademi, Arash

doi:10.17795/ijpbs-5849

Cited by 7 publications

(13 citation statements)

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“…A total of nine studies were included for the systematic review, with brief abstracts available in Appendix S3. Six were from the United States (Bergeron et al., 2019; Cnossen et al., 2017; Gupta et al., 2017; Hirata, Ovbiagele, Markovic, & Towfighi, 2016; Stromberg et al., 2019; Walker et al., 2018), one from Finland (Huttunen et al., 2016), one from Japan (Nishi et al., 2019), and one from Iran (Shafiei et al., 2017). A brief review of study design and analysis by study is included in Table 1.…”

Section: Resultsmentioning

confidence: 99%

“…Outcomes included post-concussive symptoms (Bergeron et al, 2019;Cnossen et al, 2017), functional outcome (Gupta et al, 2017;Nishi et al, 2019;Walker et al, 2018), indicators of mood and psychological symptoms (Hirata et al, 2016;Huttunen et al, 2016;Shafiei et al, 2017), and employment (Stromberg et al, 2019).…”

Section: Study Characteristicsmentioning

confidence: 99%

“…Artificial neural networks and backpropagation Are not limited by parametric formulas allowing greater flexibility and more complexity (Shafiei et al, 2017) Increasing hidden layer nodes can contribute to overfitting to the training data. Also does not benefit from feature ranking, is interpretationally complex, and computationally time-consuming (Shafiei et al, 2017) Limitations and strengths reported in this table are from information presented in the original articles. Where limitations can be overcome by study design, this is mentioned in the discussion of this review.…”

Section: Decision Treesmentioning

confidence: 99%

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A systematic review of the effectiveness of machine learning for predicting psychosocial outcomes in acquired brain injury: Which algorithms are used and why?

Mawdsley

Reynolds

Cullen

2021

Journal of Neuropsychology

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Clinicians working in the field of acquired brain injury (ABI, an injury to the brain sustained after birth) are challenged to develop suitable care pathways for an individual client's needs. Being able to predict psychosocial outcomes after ABI would enable clinicians and service providers to make advance decisions and better tailor care plans. Machine learning (ML, a predictive method from the field of artificial intelligence) is increasingly used for predicting ABI outcomes. This review aimed to examine the efficacy of using ML to make psychosocial predictions in ABI, evaluate the methodological quality of studies, and understand researchers' rationale for their choice of ML algorithms. Nine studies were reviewed from five databases, predicting a range of psychosocial outcomes from stroke, traumatic brain injury, and concussion. Eleven types of ML were employed with a total of 75 ML models. Every model was evaluated as having high risk of bias, unable to provide adequate evidence for predictive performance due to poor methodological quality. Overall, there was limited rationale for the choice of ML algorithms and poor evaluation of the methodological limitations by study authors. Considerations for overcoming methodological shortcomings are discussed, along with suggestions for assessing the suitability of data and suitability of ML algorithms for different ABI research questions.The variation in psychosocial outcomes after an acquired brain injury (ABI, an injury to the brain sustained after birth including stroke and traumatic brain injury [TBI]) challenges health and social care services to provide advice and guidance to the person, their family, and for socioeconomic implications. Currently, 'evidence-based practice' relies almost exclusively on the results of parametric analyses of group-level central tendency derived from randomized clinical trials, which offers very little guidance for individualized care. The study of clinical prediction rules to accurately predict an individual's psychosocial outcome at a future time point after ABI would serve timely resource allocation and riskThis is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Section: Resultsmentioning

confidence: 99%

Section: Study Characteristicsmentioning

confidence: 99%

Section: Decision Treesmentioning

confidence: 99%

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A systematic review of the effectiveness of machine learning for predicting psychosocial outcomes in acquired brain injury: Which algorithms are used and why?

Mawdsley

Reynolds

Cullen

2021

Journal of Neuropsychology

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“…Compared to conventional models such as the response surface methodology (RSM), these models can identify patterns between variables with minimum sum of squared errors and high correlation coefficients and without the need for limiting assumptions such as normality in studies where relationships between variables are complicated [23][24][25]. The efficiency of ANN models has been proven to solve various problems in medicine, psychology, and even humanities [26,27]. Therefore, in this study, we decided to use artificial neural network (ANN) modeling as one of the most frequently applied and powerful models in predicting and modeling general health of prisoners in order to determine the complex relationships between variables and the effect of demographic, psychological, criminological, and physical activity factors on prisoners' general health.…”

Section: Purposementioning

confidence: 99%

Predicting mental health of prisoners by artificial neural network

Allahyari¹,

Moshtagh²

2021

BioMedicine

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Introduction: Maintaining and improving prisoners' health and their rehabilitation can be effective steps towards eliminating health inequalities and approaching the UN's Sustainable Development Goals. Accordingly, identifying protective factors and health barriers of this vulnerable group and changing the prison into an environment that can deliver health interventions tailored to the needs of inmates can provide the basis for attaining justice in health. Purpose: This study builds on an artificial neural network model to determine the effect of demographic, psychological, criminological, and physical activity factors on prisoners' general health. Methods: The study detected the patterns between variables using a neural network with nine inputs and one output. To determine the neural network with the minimum sum of squared errors, we evaluated the performance of all neural networks using varying algorithms and numbers of neurons in the hidden layer. For this purpose, the analysis of the data of 149 prisoners aged between 16 and 61 years was performed using SPSS-22 software. Results: The optimal neural network model was useful in predicting prisoners' general health. In this model, the variables of occupation, life expectancy, age, and hope of acceptance were identified as the first most significant factors with 19.25, 17.45, 15.98, and 15.16 percentages, respectively, whereas the cause of incarceration, education level, type of drug misuse, and physical activity were the second most important factors with 8.82, 8.38, 7.91, and 7.04 percentages, respectively. Conclusion: Experiencing psychosocial pressures related to incarceration is more severe for the marginalized and disadvantaged individuals, persons in very young or old age ranges, and those with no history of incarceration, which can increase the likelihood of impaired health for these inmates. Consideration of the prisoners' needs in proportion to their characteristics and provision of emotional and spiritual support of the inmates, especially in the early stages of incarceration, can help shape an effective adjustment process and select appropriate and efficient strategies.

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“…An important comparison between neural network models and logistic regression shows that the main advantage of NNR models lies in their hidden layers of nodes. NNR is particularly useful when there are implicit interactions and complex relationships in data sets [ 16 ]. Some research compared Support Vector Regression (SVR), which also has the ability to catch the non-linearly relationship, with multiple regression analysis and NNR.…”

Section: Introductionmentioning

confidence: 99%

A data driven methodology for social science research with left-behind children as a case study

Wu¹,

Wang²,

Hu³

et al. 2020

PLoS ONE

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For decades, traditional correlation analysis and regression models have been used in social science research. However, the development of machine learning algorithms makes it possible to apply machine learning techniques for social science research and social issues, which may outperform standard regression methods in some cases. Under the circumstances, this article proposes a methodological workflow for data analysis by machine learning techniques that have the possibility to be widely applied in social issues. Specifically, the workflow tries to uncover the natural mechanisms behind the social issues through a data-driven perspective from feature selection to model building. The advantage of data-driven techniques in feature selection is that the workflow can be built without so much restriction of related knowledge and theory in social science. The advantage of using machine learning techniques in modelling is to uncover non-linear and complex relationships behind social issues. The main purpose of our methodological workflow is to find important fields relevant to the target and provide appropriate predictions. However, to explain the result still needs theory and knowledge from social science. In this paper, we trained a methodological workflow with left-behind children as the social issue case, and all steps and full results are included.

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Comparison of Artificial Neural Network and Logistic Regression Models for Prediction of Psychological Symptom Six Months after Mild Traumatic Brain Injury

Abstract: Background: Nowadays, outcome prediction models using logistic regression (LR) and artificial neural network (ANN) analysis have been developed in many areas of healthcare research.

Cited by 7 publications

References 24 publications

A systematic review of the effectiveness of machine learning for predicting psychosocial outcomes in acquired brain injury: Which algorithms are used and why?

A systematic review of the effectiveness of machine learning for predicting psychosocial outcomes in acquired brain injury: Which algorithms are used and why?

Predicting mental health of prisoners by artificial neural network

A data driven methodology for social science research with left-behind children as a case study

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